2018Pakistan Journal of Statistics and Operation ResearchOpen access

A New Flexible Lifetime Model with Statistical Properties and Applications

Mohamed Abo Raya

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Abstract

In this article, we introduce a new lifetime model which exhibits the increasing, the decreasing and the bathtub hazard rates. The considerable justification for the practicality of the new lifetime model is depended on the wider use of the exponentiated Weibull and Weibull lifetime models. The new lifetime model can be viewed as a mixture of the exponentiated Weibull distribution. It can also be viewed as a appropriate model for fitting the right skewed, the symmetric, the left skewed and the unimodal data. We prove empirically the importance and flexibility of the new model in modeling two types of lifetime data. The new lifetime model is a superior on the Marshall Olkin extended-Weibull, the Poisson Topp Leone-Weibull, the Burr X Exponentiated-Weibull, the Kumaraswamy-Weibull, the Gamma-Weibull, the Transmuted modified-Weibull, the Weibull-Fréchet, the Beta-Weibull, the Mcdonald-Weibull, the transmuted exponentiated generalized-Weibull, the Kumaraswamy transmuted-Weibull, and the Modified beta-Weibull models so the new model is a good substitutional to these models in modeling the aircraft windshield data. The new lifetime model is much better than the Mcdonald-Weibull, the transmuted linear exponential, the Weibull, the transmuted modified-Weibull, the Modified beta-Weibull,the transmuted additive-Weibull, the exponentiated transmuted generalized Rayleig models in modeling cancer patient data. In modeling the survival times of Guinea pigs data we deduced that the proposed model is much better than the Odd Weibull-Weibull, the Weibull Logarithmic-Weibull and the gamma exponentiated-exponential models. Finally, the new model is a preferable model than the exponentiated-Weibull, the transmuted-Weibull, the Odd Log Logistic-Weibull models, and a good alternate to these models in modeling Glass fibres data.

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What this paper is about

In this article, we introduce a new lifetime model which exhibits the increasing, the decreasing and the bathtub hazard rates. The considerable justification for the practicality of the new lifetime model is depended on the wider use of the exponentiated Weibull and Weibull lifetime models. The new lifetime model can be viewed as a mixture of the exponentiated Weibull distribution. It can also be viewed as a appropriate model for fitting the right skewed, the symmetric, the left skewed and the unimodal data. We prove empirically the importance and flexibility of the new model in modeling two types of lifetime data. The new lifetime model is a superior on the Marshall Olkin extended-Weibull, the Poisson Topp Leone-Weibull, the Burr X Exponentiated-Weibull, the Kumaraswamy-Weibull, the Gamma-Weibull, the Transmuted modified-Weibull, the Weibull-Fréchet, the Beta-Weibull, the Mcdonald-Weibull, the transmuted exponentiated generalized-Weibull, the Kumaraswamy transmuted-Weibull, and the Modified beta-Weibull models so the new model is a good substitutional to these models in modeling the aircraft windshield data. The new lifetime model is much better than the Mcdonald-Weibull, the transmuted linear exponential, the Weibull, the transmuted modified-Weibull, the Modified beta-Weibull,the transmuted additive-Weibull, the exponentiated transmuted generalized Rayleig models in modeling cancer patient data. In modeling the survival times of Guinea pigs data we deduced that the proposed model is much better than the Odd Weibull-Weibull, the Weibull Logarithmic-Weibull and the gamma exponentiated-exponential models. Finally, the new model is a preferable model than the exponentiated-Weibull, the transmuted-Weibull, the Odd Log Logistic-Weibull models, and a good alternate to these models in modeling Glass fibres data.

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Available abstract

In this article, we introduce a new lifetime model which exhibits the increasing, the decreasing and the bathtub hazard rates. The considerable justification for the practicality of the new lifetime model is depended on the wider use of the exponentiated Weibull and Weibull lifetime models. The new lifetime model can be viewed as a mixture of the exponentiated Weibull distribution. It can also be viewed as a appropriate model for fitting the right skewed, the symmetric, the left skewed and the unimodal data. We prove empirically the importance and flexibility of the new model in modeling two types of lifetime data. The new lifetime model is a superior on the Marshall Olkin extended-Weibull, the Poisson Topp Leone-Weibull, the Burr X Exponentiated-Weibull, the Kumaraswamy-Weibull, the Gamma-Weibull, the Transmuted modified-Weibull, the Weibull-Fréchet, the Beta-Weibull, the Mcdonald-Weibull, the transmuted exponentiated generalized-Weibull, the Kumaraswamy transmuted-Weibull, and the Modified beta-Weibull models so the new model is a good substitutional to these models in modeling the aircraft windshield data. The new lifetime model is much better than the Mcdonald-Weibull, the transmuted linear exponential, the Weibull, the transmuted modified-Weibull, the Modified beta-Weibull,the transmuted additive-Weibull, the exponentiated transmuted generalized Rayleig models in modeling cancer patient data. In modeling the survival times of Guinea pigs data we deduced that the proposed model is much better than the Odd Weibull-Weibull, the Weibull Logarithmic-Weibull and the gamma exponentiated-exponential models. Finally, the new model is a preferable model than the exponentiated-Weibull, the transmuted-Weibull, the Odd Log Logistic-Weibull models, and a good alternate to these models in modeling Glass fibres data.

Key concepts: Weibull distribution, Weibull modulus, Exponentiated Weibull distribution, Weibull fading, Statistics, Mathematics, Exponential function, Applied mathematics

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